Such a method is known from the article by T. L. Regulinski and Y. P. Gupta, “Reliability Cost Estimation: Managerial Perspectives”, IEEE Transactions on Reliability, Volume R-32, pages 276–281 (1983). A method is set forth there for estimating fault-induced costs in the life cycle of a system. Various failure probability distributions are postulated for the system components, depending on the fault mechanism, and their parameters are estimated statistically or by subjective expert opinion. An improved expert estimate is achieved by specifying an upper, mean and lower estimate for the failure rate and using them to determine a beta distribution of the failure rate. A unique cost estimate is calculated for the overall system from the beta distributions modeled in such a way. However, there is a problem that subjective estimates by one or a few experts lead to corrections between different failure rates, and the systematic falsification in an unknown way of the prediction of the system reliability.
Moreover, it is known that for types of fault with a constant fault rate the fault frequency, that is to say the randomness of the number of faults in a time interval, can be described by a Poisson distribution (see, for example, the textbook by A. Birolini, “Qualität und Zuverlässigkeit technischer Systeme” [Quality and Reliability of Technical Systems], Springer-Verlag Berlin (1991), page 366).
Furthermore, reliability analyses in tabular form under the terms of FMEA analysis (“Failure/Fault Modes and Effects Analysis”) or FEMCA (“Failure/Fault Modes, Effects and Criticality Analysis”) constitute prior art. The system components are listed in rows with their possible types of failure and the calculated or estimated failure rates and the susceptability to disruption of the overall system are assessed.